SynthesisNature genetics2025
Adipose tissue eQTL meta-analysis highlights the contribution of allelic heterogeneity to gene expression regulation and cardiometabolic traits.
Abstract readMeta-Analysis
In one paragraph
Synthesis in Nature genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 3 of them syntheses that pooled it.
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28citing papers in PubMed, 3 pooled it
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Who cites it
28 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- eQTL Meta-Analysis Reveals Conserved and Population-Specific Regulatory Variation Underlying Nutritional Trait Evolution and Domestication in Tomato.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Pooled it
- Skeletal muscle eQTL meta-analysis implicates genes in the genetic architecture of muscular and cardiometabolic traits.American journal of human genetics · 2025Pooled it
- Liver eQTL meta-analysis illuminates potential molecular mechanisms of cardiometabolic traits.American journal of human genetics · 2024Pooled it
- Bridging precision agriculture and human medicine through comparative genetics.Nature reviews. Genetics · 2026Review
- Article
- Design and interpretation of eQTL-GWAS colocalisation studies: Lessons from a large-scale evaluation.PLoS genetics · 2026Article
- Adipose single cell epigenome and transcriptome localize genetic risk for cardiometabolic disease and accelerated aging.Nature communications · 2026Article
- Higher eQTL power reveals signals that boost GWAS colocalization.American journal of human genetics · 2026Article
- Genome-Wide Discovery Reveals Adipose-Specific and Systemic Regulators of Insulin Resistance.medRxiv : the preprint server for health sciences · 2026Article
- Beyond the baseline: mapping the context-specific regulatory landscape of disease.Trends in genetics : TIG · 2026Review
- Lessons from single cell omics: admixed American ancestry and sex confer cardiometabolic disease risk in Mexicans.Genome medicine · 2026Article
- A meta-analysis of chromatin-associated loci provides insights into mechanistic interpretations of trait heritability.bioRxiv : the preprint server for biology · 2026Article
- Genomics link obesity and type 2 diabetes to Alzheimer's disease to unveil novel biological insights.medRxiv : the preprint server for health sciences · 2026Article
- eQTL in diseased colon tissue identifies potential target genes associated with IBD.Nature communications · 2026Article
- Genetic regulation of fatty acid content in adipose tissue.American journal of human genetics · 2026Article
- Genetic regulation of exosome biogenesis pathway in human adipose and muscle tissue and association with obesity and insulin resistance.International journal of obesity (2005) · 2026Article
- Polygenic risk score and cluster-based analysis suggests links between type 2 diabetes and vascular dementia in the KARE study.Nature communications · 2025Article
- Integrating genetic and transcriptomic data to identify genes underlying obesity risk loci.International journal of obesity (2005) · 2025Article
- The proteogenomic landscape of the human kidney and implications for cardio-kidney-metabolic health.Nature medicine · 2025 · on this mapArticle
- Diabetes mellitus polygenic risk scores: heterogeneity and clinical translation.Nature reviews. Endocrinology · 2025Review
Corrections and comments
- Update of
Authors and funding
34 authors.
Sarah M Brotman *Department of Genetics, University of North Carolina, Chapel Hill, NC, USA.ORCID 0000-0002-7566-8895
Julia S El-Sayed Moustafa *Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.ORCID 0000-0001-6963-6654
Li Guan *Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
K Alaine BroadawayDepartment of Genetics, University of North Carolina, Chapel Hill, NC, USA.
Dongmeng WangDepartment of Twin Research and Genetic Epidemiology, King's College London, London, UK.ORCID 0000-0002-0678-6556
Anne U JacksonDepartment of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI, USA.ORCID 0000-0002-9672-2547
Ryan WelchDepartment of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI, USA.ORCID 0000-0001-6378-1295
Kevin W CurrinDepartment of Genetics, University of North Carolina, Chapel Hill, NC, USA.
Max TomlinsonDepartment of Twin Research and Genetic Epidemiology, King's College London, London, UK.
Swarooparani VadlamudiDepartment of Genetics, University of North Carolina, Chapel Hill, NC, USA.
Heather M StringhamDepartment of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI, USA.ORCID 0000-0002-2991-6392
Amy L RobertsDepartment of Twin Research and Genetic Epidemiology, King's College London, London, UK.ORCID 0000-0002-5704-9249
Timo A LakkaInstitute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio, Finland.ORCID 0000-0002-9199-2871
Anniina OravilahtiInstitute of Clinical Medicine, Kuopio University Hospital, University of Eastern Finland, Kuopio, Finland.ORCID 0000-0001-8866-4482
Lilian Fernandes SilvaInstitute of Clinical Medicine, Kuopio University Hospital, University of Eastern Finland, Kuopio, Finland.ORCID 0000-0003-0225-842X
Narisu NarisuCenter for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.ORCID 0000-0002-8483-1156
Michael R ErdosCenter for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.ORCID 0000-0002-6603-1833
Tingfen YanCenter for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.
Lori L BonnycastleCenter for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.
Chelsea K RaulersonDepartment of Genetics, University of North Carolina, Chapel Hill, NC, USA.ORCID 0000-0001-5888-5196
Yasrab RazaDepartment of Twin Research and Genetic Epidemiology, King's College London, London, UK.
Xinyu YanDepartment of Twin Research and Genetic Epidemiology, King's College London, London, UK.
Stephen C J ParkerDepartment of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, USA.ORCID 0000-0001-8122-0117
Johanna KuusistoDepartment of Medicine and Clinical Research, Kuopio University Hospital, Kuopio, Finland.
Päivi PajukantaDepartment of Human Genetics and Institute for Precision Health, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.ORCID 0000-0002-6423-8056
Jaakko TuomilehtoDepartment of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland.
Francis S CollinsCenter for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.
Michael BoehnkeDepartment of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI, USA.ORCID 0000-0002-6442-7754
Michael I LoveDepartment of Genetics, University of North Carolina, Chapel Hill, NC, USA.
Heikki A KoistinenDepartment of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland.ORCID 0000-0001-7870-070X
Markku LaaksoInstitute of Clinical Medicine, Kuopio University Hospital, University of Eastern Finland, Kuopio, Finland.ORCID 0000-0002-3394-7749
Karen L MohlkeDepartment of Genetics, University of North Carolina, Chapel Hill, NC, USA. mohlke@med.unc.edu.ORCID 0000-0001-6721-153X
Kerrin S SmallDepartment of Twin Research and Genetic Epidemiology, King's College London, London, UK. kerrin.small@kcl.ac.uk.ORCID 0000-0003-4566-0005
Laura J ScottDepartment of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI, USA. ljst@umich.edu.ORCID 0000-0002-4886-5084
Funding
Genetic analysis of type II diabetes in Finnish populationZIAHG000024 · NHGRI · NATIONAL HUMAN GENOME RESEARCH INSTITUTE · PI ERDOS, MICHAEL · 2009 to 2025
$38.2MThe next iteration of the AMP-T2D Knowledge PortalUM1DK105554 · NIDDK · BROAD INSTITUTE, INC. · PI BOEHNKE, MICHAEL L, BURTT, NOEL P · 2020 to 2025
$18.5MProposal for the AMP T2D-GENES Data Coordination Center and Web PortalU01DK105554 · NIDDK · BROAD INSTITUTE, INC. · PI FLOREZ, JOSE CARLOS · 2015 to 2019
$16.8MTargeted Genetic Analysis of T2D and Quantitative TraitsR01DK072193 · NIDDK · UNIV OF NORTH CAROLINA CHAPEL HILL · PI KAREN L. MOHLKE · 2005 to 2026
$11.3MBridging the gap between type 2 diabetes GWAS and therapeutic targetsUM1DK126185 · NIDDK · UNIV OF NORTH CAROLINA CHAPEL HILL · PI CLAUSSNITZER, MELINA C, GLOYN, ANNA LOUISE · 2020 to 2024
$9.5MGenetic epidemiology of rare and regulatory variants for metabolic traitsR01DK093757 · NIDDK · UNIV OF NORTH CAROLINA CHAPEL HILL · PI KAREN L. MOHLKE · 2011 to 2026
$8.2MIdentifying Genes for Type 2 Diabetes:FUSIONR01DK062370 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI BOEHNKE, MICHAEL L, SCOTT, LAURA J. · 2003 to 2025
$6.7MNRSA IN GENETICST32GM007092 · NIGMS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI SEKELSKY, JEFF J. · 1985 to 2019
$5.9MThe Genetic Epidemiology of Heart, Lung, and Blood TraitsTraining Grant (GenHLB)T32HL129982 · NHLBI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Christy Leigh Avery, KAREN L. MOHLKE · 2016 to 2026
$3.8MLIMBIC &MEDULLARY MECH. IN COCAINE-RELATED SUDDEN DEATHR01DA006227 · NIDA · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · PI MASH, DEBORAH C. · 1990 to 2012
$3.7MMultimodal omics approach to identify health to cardiometabolic disease transitionsR01HL170604 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Paivi Pajukanta · 2023 to 2026
$2.8MMethods for Genomic Analysis in Heterogeneous TissuesR01HG010505 · NHGRI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI HALPERIN, ERAN · 2019 to 2022
$2.6MCCR NIH HHS HHSN261200800001CIntramural NIH HHS ZIA HG000024Medical Research Council MR/Y013891/1NCI NIH HHS HHSN261200800001ENHGRI NIH HHS R01 HG009976NHGRI NIH HHS R01 HG010505NHLBI NIH HHS F31 HL146121NHLBI NIH HHS F31 HL154730NHLBI NIH HHS HHSN268201000029CNHLBI NIH HHS R01 HL170604NHLBI NIH HHS T32 HL129982NIDA NIH HHS R01 DA006227NIDDK NIH HHS R01 DK062370NIDDK NIH HHS R01 DK072193NIDDK NIH HHS R01 DK093757NIDDK NIH HHS R01 DK132775NIDDK NIH HHS U01 DK105554NIDDK NIH HHS U01 DK105561NIDDK NIH HHS UM1 DK105554NIDDK NIH HHS UM1 DK126185NIGMS NIH HHS T32 GM007092NIMH NIH HHS R01 MH090936NIMH NIH HHS R01 MH090937NIMH NIH HHS R01 MH090941NIMH NIH HHS R01 MH090948NIMH NIH HHS R01 MH090951NIMH NIH HHS R01 MH101782NIMH NIH HHS R01 MH101810NIMH NIH HHS R01 MH101814NIMH NIH HHS R01 MH101819NIMH NIH HHS R01 MH101820NIMH NIH HHS R01 MH101822NIMH NIH HHS R01 MH101825RCUK | Medical Research Council (MRC) MR/M004422/1RCUK | Medical Research Council (MRC) MR/R023131/1U.S. Department of Health & Human Services | National Institutes of Health (NIH) F31HL146121U.S. Department of Health & Human Services | National Institutes of Health (NIH) F31HL154730U.S. Department of Health & Human Services | National Institutes of Health (NIH) R01DK062370U.S. Department of Health & Human Services | National Institutes of Health (NIH) R01DK072193U.S. Department of Health & Human Services | National Institutes of Health (NIH) R01DK093757U.S. Department of Health & Human Services | National Institutes of Health (NIH) R01DK132775U.S. Department of Health & Human Services | National Institutes of Health (NIH) R01HG009976U.S. Department of Health & Human Services | National Institutes of Health (NIH) R01HG010505U.S. Department of Health & Human Services | National Institutes of Health (NIH) R01HL170604U.S. Department of Health & Human Services | National Institutes of Health (NIH) T32GM007092U.S. Department of Health & Human Services | National Institutes of Health (NIH) T32HL129982U.S. Department of Health & Human Services | National Institutes of Health (NIH) U01DK105561U.S. Department of Health & Human Services | National Institutes of Health (NIH) UM1DK105554U.S. Department of Health & Human Services | National Institutes of Health (NIH) UM1DK126185U.S. Department of Health & Human Services | National Institutes of Health (NIH) ZIAHG000024Wellcome Trust
Abstract
Complete characterization of the genetic effects on gene expression is needed to elucidate tissue biology and the etiology of complex traits. In the present study, we analyzed 2,344 subcutaneous adipose tissue samples and identified 34,774 conditionally distinct expression quantitative trait locus (eQTL) signals at 18,476 genes. Over half of eQTL genes exhibited at least two eQTL signals. Compared with primary eQTL signals, nonprimary eQTL signals had lower effect sizes, lower minor allele frequencies and less promoter enrichment; they corresponded to genes with higher heritability and higher tolerance for loss of function. Colocalization of eQTLs with genome-wide association study (GWAS) signals for 28 cardiometabolic traits identified 1,835 genes. Inclusion of nonprimary eQTL signals increased discovery of colocalized GWAS-eQTL signals by 46%. Furthermore, 21 genes with ≥2 colocalized GWAS-eQTL signals showed a mediating gene dosage effect on the GWAS trait. Thus, expanded eQTL identification reveals more mechanisms underlying complex traits and improves understanding of the complexity of gene expression regulation.
Indexed as
Adipose TissueCardiovascular DiseasesGene Expression RegulationGenetic HeterogeneityQuantitative Trait LociAllelesGene FrequencyGenome-Wide Association StudyHumansMalePolymorphism, Single Nucleotide
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